Cancer survival, incidence and mortality by Area Health Service in NSW 1994 to 2000

Transcription

1 Cancer survival, incidence and mortality by in NSW 1994 to 2000 Xue Q Yu Dianne O Connell Robert Gibberd David Smith Bruce Armstrong Cancer Epidemiology Research Unit Cancer Research and Registers Division The Cancer Council NSW May 2003

2 Acknowledgments The authors would like to thank Professor John Beard, Dr Michael Coory and Dr Hanna Noworytko for reviewing an ealier draft of this report and providing constructive advice. The Cancer Epidemiology Research Unit is a unit of and is directly funded by The Cancer Council NSW. The NSW Central Cancer Registry is managed and operated by The Cancer Council NSW under contract to NSW Health. National Library of Australia Cataloguing-in-Publication data: Cancer survival, incidence and mortality by in NSW 1994 to ISBN Key words: Cancer survival, Relative survival, Cancer incidence, Cancer mortality, Regional variation, Empirical Bayes, New South Wales, Australia Suggested citation: Yu XQ, O Connell DL, Gibberd RW, Smith DP, Armstrong BK. Cancer survival, incidence and mortality by in NSW 1994 to Sydney: The Cancer Council NSW, Published by The Cancer Council New South Wales, May 2003 Also in this series: Breast Cancer Survival in NSW in 1973 to 1995 (1998) Geographic distribution of cancer in New South Wales in 1991 to 1995 by Local Government Area (1999) Survival from Cancer in New South Wales in 1980 to 1995 (1999) Remoteness and cancer incidence, mortality and survival in New South Wales 1992 to 1996 (2002) Cancer Epidemiology Research Unit Cancer Research and Registers Division Cancer Council NSW Locked Mail Bag 1 KINGS CROSS NSW 1340 Telephone: (02) Facsimile: (02) crrd@nswcc.org.au Internet:

3 Contents Summary...1 Cancer survival... 1 Lives potentially savable... 1 Cancer incidence... 1 Cancer mortality... 1 Introduction...6 Methods...8 Data sources... 8 Incidence data... 8 Mortality data... 8 Survival data... 8 Population data... 8 Spread of disease at diagnosis... 9 of residence... 9 Indirect age standardisation...10 Excess number of new cases or deaths and confidence intervals...10 Five-year relative survival...10 Adjusted relative risk of excess death and confidence intervals...11 Excess deaths due to cancer...11 Empirical Bayes method...12 Potential lives savable...12 Guide to interpretation of results...13 Results...14 All cancers...14 Head and neck cancer...16 Cancer of the oesophagus...18 Stomach cancer...20 Colon cancer...22 Rectal cancer...24 Liver cancer...26 Gallbladder cancer...28 Cancer of the pancreas...30 Lung cancer...32 Mesothelioma...34 Female breast cancer...36 Melanoma of the skin...38 Cervical cancer...40 Uterine cancer...42 Ovarian cancer...44 Prostate cancer...46 Testis cancer...48 Bladder cancer...50 Kidney cancer...52 Brain cancer...54 Thyroid cancer...56 Non-Hodgkin lymphoma...58 Hodgkin s disease...60 Multiple myeloma...62 Leukaemia...64 Implications...66 References...67 Appendixes...68 Appendix one...68 Appendix two...69 Appendix three...71 Appendix four...74

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5 Cancer survival, incidence and mortality in NSW Summary Summary Five-year relative survival by in New South Wales (NSW) during 1994 to 2000 was calculated for 25 major cancers. Multiple regression models of relative survival were used to estimate the effect of place of residence (grouped into s) at the time of diagnosis on patients survival while adjusting for potential confounders. The relative risk of excess death was estimated for each using the State average risk as the reference. Relative survival compares the actual survival in the cancer patients with that of people in the general population of the same age, sex and place of residence. To support the survival statistics, information about incidence and mortality during was included. The analysis took into account sampling error for individual s through Empirical Bayes methods and an overall statistical test for regional variation in survival was conducted to determine whether there were significant differences between areas. Cancer survival Significant variation in relative survival was found for nine of the 25 cancer sites analysed: cancers of the colon, liver, lung, breast, ovary and prostate; melanoma of the skin; and multiple myeloma and leukaemia. The s that differed significantly from the State average are shown in Table 1. While there was significant regional variation in relative survival for multiple myeloma, no single was significantly different from the State average. For all cancers combined, patients from four rural areas and Western Sydney and Wentworth experienced higher risk of excess death than the State as a whole, while Central, Northern and South Eastern Sydney, and Northern Rivers had less excess deaths than expected from the State average in this period. Lives potentially savable The number of lives potentially savable provides an estimate of how many additional cancer patients would survive for at least five years after diagnosis if the relative risks of excess death were decreased across areas. It reflects both area variation in relative survival and the size of the burden of the cancer in the population. It is a theoretical estimate only and does not imply that all of these deaths would be postponed by applying current methods for preventing and treating cancer. As shown in Table 2, a total of 2,903 additional people with one of the 25 major cancers (6.4% of excess deaths due to cancer) could be expected to survive five years if cancer survival was improved. The three cancers with the largest potential gains are cancers of the lung, colon and prostate. Cancer incidence Table 3 shows a summary of the variation in cancer incidence across s. s with small populations were generally not significantly different from the State average whereas differences were suggested for some of the larger metropolitan areas. Cancer mortality Table 4 shows a summary of the variation in cancer mortality across s. Due to smaller numbers of deaths, there was less regional variation with the main differences being observed for the metropolitan s. The Cancer Council NSW 1

6 Summary Cancer survival, incidence and mortality in NSW Table 1: Summary of comparisons across s for the adjusted relative risk of excess death * by cancer site in NSW Cancer site Central Sydney Northern Sydney Western Sydney Wentworth S/Western Sydney Central Coast Hunter Illawarra S/Eastern Sydney Northern Rivers Mid North Coast New England Macquarie Mid Western Far West Greater Murray Southern Head and neck Oesophagus Stomach Colon Rectum Liver Gallbladder Pancreas Lung Mesothelioma Breast (female) Melanoma of the skin Cervix Body of uterus Ovary Prostate Testis Bladder Kidney Brain Thyroid Non-Hodgkin lymphoma Hodgkin s disease Multiple myeloma Leukaemia All cancers# More excess deaths Fewer excess deaths No significant difference in number of excess deaths * Empirical Bayes estimates adjusted for age, sex (if appropriate), spread of disease at diagnosis, follow-up year and interaction terms as required. # Includes all 25 sites except for testis cancer. 2 The Cancer Council NSW

7 Cancer survival, incidence and mortality in NSW Summary Table 2: Summary of comparisons across s for the number of lives potentially savable over the first five years after diagnosis by cancer site in NSW Central Sydney Northern Sydney Western Sydney Wentworth S/Western Sydney Central Coast Hunter Illawarra Cancer site n % Head and neck Oesophagus Stomach Colon Rectum Liver Gallbladder Pancreas Lung Mesothelioma* Breast (female) Melanoma of the skin Cervix Body of uterus Ovary Prostate Testis Bladder Kidney Brain Thyroid Non-Hodgkin lymphoma Hodgkin s disease Multiple myeloma Leukaemia Total S/Eastern Sydney Northern Rivers Mid North Coast New England Macquarie Mid Western Far West Greater Murray Southern New South Wales * Based on two-year relative survival. Percentage of total excess cancer deaths. Sum of all 25 sites. The Cancer Council NSW 3

8 Summary Cancer survival, incidence and mortality in NSW Table 3: Summary of comparisons across s for agestandardised incidence by cancer site and sex in NSW Cancer site Males Head and neck Oesophagus Stomach Colon Rectum Liver Gallbladder Pancreas Lung Mesothelioma Melanoma of the skin Prostate Testis Bladder Kidney Brain Thyroid Non-Hodgkin lymphoma Hodgkin s disease Multiple myeloma Leukaemia All cancers Central Sydney Northern Sydney Western Sydney Wentworth S/Western Sydney Central Coast Hunter Illawarra S/Eastern Sydney Northern Rivers Mid North Coast New England Macquarie Mid Western Far West Greater Murray Southern Females Head and neck Oesophagus Stomach Colon Rectum Liver Gallbladder Pancreas Lung Mesothelioma Breast Melanoma of the skin Cervix Body of uterus Ovary Bladder Kidney Brain Thyroid Non-Hodgkin lymphoma Hodgkin s disease Multiple myeloma Leukaemia All cancers More new cases than expected. Fewer new cases than expected. No significant difference in number of new cases. 4 The Cancer Council NSW

9 Cancer survival, incidence and mortality in NSW Summary Table 4: Summary of comparisons across s for age-standardised mortality by cancer site and sex in NSW Cancer site Males Head and neck Oesophagus Stomach Colon Rectum Liver Gallbladder Pancreas Lung Mesothelioma Melanoma of the skin Prostate Testis Bladder Kidney Brain Thyroid Non-Hodgkin lymphoma Hodgkin s disease Multiple myeloma Leukaemia All cancers Central Sydney Northern Sydney Western Sydney Wentworth S/Western Sydney Central Coast Hunter Illawarra S/Eastern Sydney Northern Rivers Mid North Coast New England Macquarie Mid Western Far West Greater Murray Southern Females Head and neck Oesophagus Stomach Colon Rectum Liver Gallbladder Pancreas Lung Mesothelioma Breast Melanoma of the skin Cervix Body of uterus Ovary Bladder Kidney Brain Thyroid Non-Hodgkin lymphoma Hodgkin s disease Multiple myeloma Leukaemia All cancers More deaths than expected. Fewer deaths than expected. No significant difference in number of deaths. The Cancer Council NSW 5

10 Introduction Cancer survival, incidence and mortality in NSW Introduction Patient survival is an indicator of the quality of cancer management including early diagnosis and use of effective treatments. Relative survival at five years after diagnosis is the measure of cancer survival most often used which compares survival in cancer patients with that for a comparable group in the general population. An analysis of cancer survival across regions is useful to highlight possible differences. Further work is then required to seek possible explanations for any differences observed including differences in patient mix (in terms of age, sex and socio-economic factors) and differences across s in methods of diagnosis and treatment. It can be used to identify areas where cancer control efforts may need to be more concentrated. Regional variation in cancer survival has been reported in the US (Farrow et al. 1996, Goodwin 2002), Canada (The Canadian Institute for Health Information 2002), England (Cartman et al. 2002), Scotland (Twelves et al. 2001, Campbell et al. 2000), Italy (Gatta et al. 1997) and Denmark (Madsen et al. 2002). Place of residence was found to be an important determinant of survival in women with breast cancer in both Canada and Scotland (The Canadian Institute for Health Information 2002, Twelves et al. 2001). Using population-based registry data, studies in England and Denmark found that survival from lung cancer was dependent on place of residence (Gatta et al. 1997, Madsen et al. 2002). Data for from the Surveillance, Epidemiology and End Results (SEER) Program in the US, suggested significant regional variation in survival for cancers of the colon, rectum, breast, uterus and prostate across SEER areas (Farrow et al. 1996). However, a number of factors make it difficult to interpret the results of such analyses. These factors include an individual s ability to access primary diagnostic care, and the availability of diagnostic and treatment facilities in different areas. In addition, differences in cancer survival may be due to differences in treatment, or they may be due simply to the addition of lead time through earlier detection and diagnosis of cancers. Survival may be increased due to more effective treatment. A good example is the dramatic improvement in survival of childhood leukaemia since the late 1970s, which is entirely the result of improved treatment. Thus, different treatment protocols across areas could result in regional variation in survival rates. When a screening test advances the time of diagnosis without changing the time of death, the measured survival will be increased dramatically. For example, consider a man diagnosed with prostate cancer in 1996 who then died in If this man received a prostate-specific antigen (PSA) test that led to a prostate cancer diagnosis in 1994, but he still died in 1998, then the survival time would be increased from two years to four years despite no real change in disease outcome. Thus, differences in screening rates across areas could affect the comparison of survival rates between regions. If early treatment is effective and more people with early-stage cancer are detected by screening programs, then there will be a real increase in the survival rate. Good examples of cancers characterised by better response when treated at an early stage are colorectal and breast cancer. If both the treatment regimen and screening coverage are different between areas then regional variation in cancer survival will result. When survival rates are calculated for areas with small populations, the estimates may be affected by sampling error and the estimates for individual areas may lack precision. Also, 6 The Cancer Council NSW

11 Cancer survival, incidence and mortality in NSW Introduction sampling error in the observed survival for areas with small populations can mask true regional variation in cancer survival. Consequently, use of these estimates may introduce errors in decision making for health service planning. To control for the effects of sampling error, Empirical Bayes (EB) techniques can be used to estimate the systematic variation across regions and provide more precise estimates for individual areas (Greenland and Robins 1991, Rothman and Greenland 1998). Basically this approach assumes that the excess risk of death after a diagnosis of cancer for a particular area is similar to that for other areas under comparison. The area-specific risk is then combined with those of other areas to produce an estimate that is less affected by sampling error (a shrunken estimator). In this way, each area can borrow strength from other areas in estimating its area-specific risk. The resulting area-specific estimates are more precise due to the use of additional information from other areas. Little is known about how place of residence affects people s survival after diagnosis of cancer in Australia. In this report, we address this issue by using the most recent available data from the New South Wales Central Cancer Registry (CCR) and EB techniques to estimate regional variation in cancer survival across NSW s. In addition, the number of potential lives that could be extended beyond five years after diagnosis of cancer were estimated to indicate where the greatest gains could be achieved. These were estimated by shifting the mean of the distribution of the relative risk of excess death down to the 20th centile. We also present data on the regional variation in cancer incidence and mortality as background information. The purpose of the report is to present the information on variation in cancer survival, incidence and mortality across s as obtained from an analysis of the data from the NSW CCR. As information recorded about patients is limited and information on investigations of treatments provided is not collected, any discussion of the reasons for the observed variation would be speculative. Therefore, we have kept discussion and interpretation of the findings to a minimum. The results should be considered as hypothesisgenerating rather than hypothesis-testing. Any significant differences found across Area Health Services should be investigated through other means to identify the possible explanations for the differences. The Cancer Council NSW 7

12 Methods Cancer survival, incidence and mortality in NSW Methods Data sources Cancer data for this report were obtained from the NSW Central Cancer Registry (CCR). The Registry is a population-based register of all cancers diagnosed in NSW residents and has been operating since Notification is mandatory from public and private hospitals, departments of radiation oncology, nursing homes, pathology laboratories, outpatient departments, day procedure centres and the Registrar of Births, Deaths and Marriages. The Cancer Council NSW manages the Registry, which is funded by the NSW Department of Health 1. Incidence data People included in the summaries of cancer incidence were those diagnosed with at least one cancer in the 25 major sites and who were resident in NSW at the time of diagnosis between 1 January 1994 and 31 December Data for only the first invasive cancer diagnosed in a person during this time period were included. Mortality data Mortality data were compiled from notifications to the NSW CCR mainly from the Principal Registrar, Births, Deaths and Marriages (NSW). The mortality data were based on place of residence at the time of diagnosis, rather than at the time of death from the cancer, and may therefore differ slightly from analyses of mortality data published by the Australian Bureau of Statistics (ABS). The mortality data for the cancer sites reported here were based on the deaths that occurred during 1994 to Survival data Deaths from any causes in people with cancer were compiled by passive follow-up. All people with cancer who were not known to be dead were matched against death records up to 31 December 2000 from the Principal Registrar, Births, Deaths and Marriages (NSW). Matching was also undertaken between cancer cases and all deaths registered in Australia up to 31 December 2000 and recorded on the National Death Index compiled by the Australian Institute of Health and Welfare. Probabilistic matching was used with a multi-pass INTEGRITY algorithm (Vality Technology Incorporated 2000). Equivocal matches were investigated by individual examination of the details available and by active follow-up where necessary. However, no direct contact was made with people with cancer or their families. In the survival analyses reported here, it was assumed that those cases not known to be dead were alive. Population data Population data for 1994 to 1998 were derived from ABS population estimations. The ABS provides annual estimates of the resident population (ERP) at 30 June by five-year age groups, sex, calendar year and Statistical Local Area (SLA). populations were derived by aggregating the appropriate SLA-level ERPs, except in the cases of Central Sydney and South Eastern Sydney s, the border between which transects two SLAs. ERPs for these SLAs were apportioned according to the proportions derived from the 1 For more information about the NSW Central Cancer Registry see Cancer in New South Wales. Incidence and Mortality 2000 (Tracey & Supramaniam 2002). 8 The Cancer Council NSW

13 Cancer survival, incidence and mortality in NSW Methods 1996 Census usual resident counts at the Collection District level and aggregated with the whole SLAs in each of the two Areas. Population data for this report were obtained from the Health Outcomes Information and Statistical Toolkit (HOIST). HOIST 2 contains many data sources commonly used for population health surveillance in NSW. Spread of disease at diagnosis The NSW CCR requests information for all notifications on the spread of cancer at diagnosis according to a simple classification. Data on degree of spread for most tumours were recorded from notifiers as: localised to tissue of origin (localised), invasion of adjacent organs or regional lymph nodes (regional) and distant metastases (distant). In a number of cases, the degree of spread at diagnosis could not be ascertained and was coded as unknown. This staging is not as detailed as the standard TNM staging system 3 based on the extent of the primary tumour (T), regional lymph nodes (N) and distant metastases (M). Spread of disease is not relevant for lymphomas, multiple myeloma and leukemia. Therefore it was not included in the analyses for these cancers. It was also omitted from the analysis of brain cancers as almost all were localised to the brain. of residence The geographical variation in cancer survival, incidence, and mortality in NSW in is described across the 17 s in NSW. Nine cover the major urban areas with larger populations ranging from 270,000 to 750,000 and eight rural s with populations ranging from 50,000 to 250,000. Assignment of cases and deaths to s was based on the Local Government Area (LGA) of the place of residence at the time of the diagnosis of cancer. The Area Health Service boundaries defined by the NSW Department of Health in 1996 were used. The following two maps show the current boundaries of the NSW s. Each is made up of a number of LGAs, which are listed in Appendix 2. Figure 1: Map of NSW s 2 HOIST is a SAS-based data warehouse operated by Centre for Epidemiology and Research of the NSW Department of Health. 3 Condensed TNM for Coding the Extent of Disease recommended by the European Network of Cancer Registries, April The Cancer Council NSW 9

14 Methods Cancer survival, incidence and mortality in NSW Indirect age standardisation Age standardisation of incidence and mortality rates was performed using the indirect method. The observed numbers of new cases or deaths represent all new cancers diagnosed or cancer deaths occurring in a specified between 1994 and The expected numbers of new cases or deaths were calculated using the age-specific rates for NSW multiplied by the age-specific population of the of interest to generate the expected number of events for each age group. These age-specific expected numbers were summed across age groups to get the total expected number for each. The column of expected numbers of new cases or deaths may not add to the total shown as numbers were rounded. Excess number of new cases or deaths and confidence intervals The excess number of new cases or deaths is the difference between the corresponding observed and expected numbers. A positive excess number indicates that the Area Health Service had more events than expected and a negative number indicates that the Area Health Service had fewer events than expected. The excess numbers of new cases or deaths were shrunk using the Empirical Bayes method described below to obtain better area-specific estimates that were less affected by sampling error. The excess was considered to be statistically significantly more or less than expected if its 95% confidence interval did not include zero. The 95% confidence interval is an interval (or range of values) which we can be 95% certain contains the true excess number of new cases or deaths. Confidence intervals provide an indication of the level of statistical uncertainty in the true excess number. A modification of the method described by Ulm was used (Ulm 1990). The confidence limits for the observed number of events were calculated using the Poisson distribution and then the expected number of events (which was treated as a constant) was subtracted from these limits. Five-year relative survival This report describes five-year relative survival of people in each in NSW (Estėve et al. 1994). Relative survival is the ratio of the observed survival rate experienced by cancer patients over a specified interval of time after diagnosis to the expected survival rate in a comparable group of people (in terms of age and sex) from the general population. The life table for the corresponding was used to calculate the expected survival. A relative survival of 100% in a particular group indicates that the cancer has had no effect on the survival of that group. A relative survival of 80% in a particular group indicates that 20% more people diagnosed with cancer had died during the five years than in a comparable group of people from the general population. If the survival analysis was restricted to patients diagnosed between 1994 and 1998, five-year survival would have been available only for the 1994 and 1995 patient cohorts because followup was carried out only to the end of Therefore, to supplement the information from the patients diagnosed between 1994 and 1998, we used the later survival experience of patients diagnosed between 1991 and This method of analysis, called period analysis, reflects the most recent survival experience based on the deaths in the period 1994 to 2000 (Brenner and Gefeller 1996). Patients diagnosed in contributed to the first two years of survival after diagnosis, those diagnosed contributed to the third year, those diagnosed contributed to the fourth year and those diagnosed provided five-year survival (Brenner and Hakulinen 2002). The years of diagnosis and years of follow-up used in 10 The Cancer Council NSW

15 Cancer survival, incidence and mortality in NSW Methods the analysis of relative survival are illustrated in Figure 2 showing that the survival experience refers to the years between 1994 and Figure 2: Patients included in the estimation of relative survival using the period method Follow-up interval (year) Year of diagnosis/year of follow-up / / / / / / / / / / / / / / / / / / / / / / / / /2000 The survival analysis was limited to people under 90 years of age at diagnosis to avoid misclassification of vital status that can occur in very elderly people. People who were notified to the cancer registry by death certificate only or who were diagnosed post mortem were excluded, as survival time for these cases was effectively zero. Adjusted relative risk of excess death and confidence intervals Adjusted relative risks of excess death due to a diagnosis of cancer are presented for each Area Health Service. Relative risk indicates the risk of excess death within five years after diagnosis compared to that of the reference category which, in this analysis, was the State average. The higher the relative risk of excess death, the lower the chance of surviving the first five years after a diagnosis of cancer. The relative risk of excess death was calculated from the regression coefficients of a generalised linear model. As relative risk may be influenced by factors such as age, sex, year of follow-up and spread of disease at diagnosis, the model was fitted with these variables as covariates to produce adjusted relative risks for each. To test the assumption of proportional excess hazards over age or spread of disease, interaction terms involving these variables and year of follow-up were fitted in the model together with the main effects when convergence of the model was achieved. For most cancer sites age was divided into four groups: years, years, years and years. Age groups used for the analysis of specific cancers are described in Appendix 3. The follow-up period was stratified into 12-month intervals. Point estimates and 95% confidence intervals for the relative risks were calculated from the estimated coefficients for each and are presented in the tables in this report. Confidence intervals indicate the uncertainty about the true relative risk of excess death in the population. Wide confidence intervals reflect small numbers of observed cases and small populations at risk and hence increased uncertainty about the true value. Confidence intervals that did not include one (1) were taken to indicate that the relative risk of excess death was statistically significantly higher or lower than expected from the State average. These results are included in Appendix 4. Excess deaths due to cancer The death rates of cancer patients are usually higher than those of the general population. The number of excess deaths provides a measure of the burden of mortality from cancer within five years of diagnosis. It provides an estimate of the difference between the actual number of The Cancer Council NSW 11

16 Methods Cancer survival, incidence and mortality in NSW additional deaths occurring within five years of a cancer diagnosis in each and the number that would be expected based on the State average risk. Empirical Bayes method The estimated relative risk of excess death was highly uncertain for some rare cancers especially in s with small populations (such as the Far West Area Health Service). The Empirical Bayes (EB) method was used to shrink the relative risk and to reduce the uncertainty of estimates in areas with small populations. The EB method combines the information about the individual area-specific risk with information about risk from all other areas in the study. The resulting relative risk takes the form of a weighted average of the observed risk for an individual area and the mean relative risk across areas. On applying this approach, formerly unstable estimates became less extreme and more precise, whereas moderate, stable estimates remained much the same. For cancers with large numbers of events, such as lung and colon cancer, the EB estimates from areas with large populations were similar to the estimates from the standard approach. On the other hand, the EB estimates were shrunk towards the State average (which is set to 1) for rare cancers such as thyroid and liver cancer for areas with small populations. The method is described in greater detail in Appendix 3. The estimated unshrunken relative risks of excess death for each cancer site by are shown in Appendix 4. For some cancer sites, because area variation was so small, the shrunken estimates of the adjusted relative risk of excess death or the excess number of new cases or deaths were all equal to the null values of 1 or 0 (respectively). Also the 95% confidence limits were very close to 1 or 0. In this situation, it has been indicated in the corresponding columns of the table that there was no area variation. In other situations the results for each are shown even though there was no statistically significant regional variation. A p-value less than 0.05 corresponding to the statistical test for regional variation was taken to indicate significant differences across s. Only differences across Area Health Services that reached statistical significance are reported and discussed in the text. Potential lives savable The number of lives savable was calculated by shifting the mean relative risk of excess death after a diagnosis of cancer to the 20th centile of the distribution. This represents the additional number of cancer patients that would survive five years after diagnosis if cancer survival was improved across the State. It reflects both area variation in relative survival and the size of the burden of the cancer in the population. While there may be significant regional variation for some cancer sites, the overall potential gains may not be sufficient to expend resources in improving survival for that cancer. However, for some of the cancer sites with large numbers of patients, there may be considerable gains to be made through small improvements. While the choice of the 20th centile is somewhat arbitrary, it was a recommended reporting format considered to be a more realistic and achievable target for improvement in quality of care (Gibberd et al. 2000). The column totals shown for the number of lives savable may vary from the sum of the individual entries due to rounding of numbers for each. 12 The Cancer Council NSW

17 Cancer survival, incidence and mortality in NSW Methods Guide to interpretation of results The quantities presented in the tables for survival, incidence and mortality are explained in the order in which they occur across the columns. Five-year relative survival is the ratio of the observed survival rate experienced by cancer patients over five years to the expected survival rate in a comparable group of people (in terms of age, sex and (AHS) of residence) from the general population. Relative risk of excess death provides a measure for comparing risk of excess death after a diagnosis of cancer across AHSs while adjusting for other factors that may affect survival. The State average risk was used as the reference and set to a value of 1. The higher the relative risk the lower the chance of surviving five years, and the lower the relative risk the higher the chance of surviving five years. For example, area A with a relative risk of 1.5 indicates that patients from that area experienced a 50% higher risk of excess death than those in the State as a whole. For area B with a relative risk of 0.5, the risk of excess death was 50% lower than the State average. Excess number of cancer deaths is the difference in the observed number of excess deaths in a given area due to a diagnosis of cancer and the number that would be expected if the risk of excess death in that area was the same as the State average. A positive excess number indicates that the AHS had more deaths in cancer patients within five years of diagnosis than expected from the State average and a negative number indicates that the AHS had fewer excess deaths within five years than expected. The number of potential lives savable represents the additional number of cancer patients who would survive up to five years after diagnosis. These could be used to identify cancer sites in which improvement in cancer care could result in large gains in survival. Number of observed new cases is the number of new cases diagnosed in each AHS during and notified to the registry. Expected number of new cases (or deaths) was calculated using indirect age standardisation and the rates for the whole of NSW as the standard. It represents the number of events that would have been reported in each AHS if its rate was the same as that of NSW. Excess number of new cases (or deaths) is the difference between the observed and expected numbers of new cases or deaths. The estimates were shrunk to the mean of the distribution using the EB method and represent the magnitude of the difference between the area-specific and State average incidence (or mortality) rates. Percent of new cases aged 65 years or over at diagnosis is the number of new cases aged 65 years or over divided by the total number of new cases and multiplied by 100. This allows a comparison to be made of the age-structure of the cancer patients across AHSs. Percent of new cases diagnosed as non-localised is the number of new cases that were nonlocalised (regional or distant spread) at diagnosis as a proportion of the total number of new cases, excluding cases with unknown stage of disease at diagnosis. It is an indicator of the severity of cancer at diagnosis in each of the AHSs. Number of observed deaths is the number of cancer deaths that occurred in each AHS during and identified by the registry. The Cancer Council NSW 13

18 All cancers Cancer survival, incidence and mortality in NSW Results All cancers This category includes all cancers in this report except testis cancer The five-year relative survival from all these cancers in NSW between 1994 and 2000 was 61.0% The adjusted relative risks of excess death after shrinking were higher than expected in Western Sydney (6%, 203 excess deaths), Wentworth (11%, 155 excess deaths), New England (7%, 90 excess deaths), Macquarie (9%, 65 excess deaths), Mid Western (12%, 131 excess deaths) and Southern (6%, 75 excess deaths); and lower than expected in Central Sydney (4%, 130 fewer deaths), Northern Sydney (12%, 700 fewer deaths), South Eastern Sydney (9%, 542 fewer deaths) and Northern Rivers (5%, 111 fewer deaths) If the State average risk of excess death was shifted to the 20 th centile of the distribution, an additional 2,088 cancer patients would be expected to survive to five years after diagnosis the largest numbers being in South Eastern Sydney and Northern Sydney There were 124,369 new cases of all cancers in NSW in 1994 to 1998 (68,586 males and 55,783 females) and 52,278 deaths (29,921 males and 22,357 females) There were more new cases of cancer than expected in Northern Sydney (294 in females), Central Coast (303 in males, 126 in females), Illawarra (161 in males), South Eastern Sydney (247 in females) and Greater Murray (160 in males); and fewer than expected in Northern Sydney (286 in males), Western Sydney (166 in females), South Western Sydney (167 in males, 226 in females), Northern Rivers (83 in females) and New England (95 in males) There were more deaths from all cancers than expected after shrinking in Central Sydney (103 in females), Hunter (119 in males), Illawarra (82 in males) and New England (42 in females); and fewer than expected in Northern Sydney (482 in males), Northern Rivers (93 in males, 109 in females) and Mid North Coast (78 in females) Table 5a: Five-year relative survival, relative risk of excess death due to cancer and excess cancer deaths after shrinking with 95% confidence intervals and potential lives savable by Area Health Services in NSW All cancers Five-year relative Relative risk of excess Excess number of Potential survival (%) death (95% CI) cancer deaths (95% CI) lives savable Central Sydney ( ) -130 (-244,-17) 163 Northern Sydney ( ) -700 (-839,-560) 241 Western Sydney ( ) 203 (86,320) 176 Wentworth ( ) 155 (83,228) 74 South Western Sydney ( ) 29 (-97,155) 202 Central Coast ( ) -26 (-122,71) 122 Hunter ( ) -67 (-190,56) 190 Illawarra ( ) 9 (-91,109) 130 South Eastern Sydney ( ) -542 (-687,-397) 260 Northern Rivers ( ) -111 (-197,-25) 97 Mid North Coast ( ) 12 (-80,103) 110 New England ( ) 90 (25,155) 61 Macquarie ( ) 65 (17,113) 36 Mid Western ( ) 131 (68,194) 58 Far West ( ) 14 (-17,44) 18 Greater Murray ( ) 23 (-57,103) 86 Southern ( ) 75 (7,143) 66 New South Wales Note: Relative risk of excess death compares observed relative survival with that expected from a Poisson model including terms for age, sex, follow-up year, site and spread of disease at diagnosis, and site and spread of disease by follow-up year interaction terms, with the NSW average rates as the reference. All sites except testis cancer combined as one group. 14 The Cancer Council NSW

19 Cancer survival, incidence and mortality in NSW All cancers Table 5b: Observed and expected number of new cases and deaths, excess new cases and deaths with 95% confidence intervals, percent of cases aged 65 years or over and with non-localised spread of disease at diagnosis by Area Health Services in NSW All cancers Males Number of new cases % diagnosed % diagnosed Number of deaths observed expected excess* (95% CI) 65+ yrs as non-localised observed expected excess* (95% CI) Central Sydney (-228,33) (-43,133) Northern Sydney (-458,-114) (-591,-373) Western Sydney (-271,3) (-32,150) Wentworth (-141,22) (-46,61) South Western Sydney (-313,-21) (-4,190) Central Coast (189,417) (-5,147) Hunter (-4,288) (21,217) Illawarra (44,278) (4,159) South Eastern Sydney (-157,191) (-97,134) Northern Rivers (-78,129) (-160,-26) Mid North Coast (-31,191) (-122,23) New England (-169,-22) (-53,46) Macquarie (-57,45) (-6,63) Mid Western (-106,35) (-21,75) Far West (-41,21) (-7,35) Greater Murray (63,257) (-20,107) Southern (-86,74) (-81,23) New South Wales Females Number of new cases % diagnosed % diagnosed Number of deaths observed expected excess* (95% CI) 65+ yrs as non-localised observed expected excess* (95% CI) Central Sydney (-137,93) (27,178) Northern Sydney (130,459) (-185,22) Western Sydney (-290,-42) (-41,115) Wentworth (-98,50) (-32,58) South Western Sydney (-356,-97) (-108,52) Central Coast (32,220) (-74,46) Hunter (-58,194) (-1,163) Illawarra (-17,177) (-62,60) South Eastern Sydney (90,405) (-38,163) Northern Rivers (-165,-1) (-160,-58) Mid North Coast (-111,63) (-133,-24) New England (-77,48) (0,83) Macquarie (-55,26) (-15,38) Mid Western (-103,14) (-28,49) Far West (-35,10) (-8,22) Greater Murray (-117,39) (-91,8) Southern (-107,18) (-58,22) New South Wales * Excess number after shrinking. The Cancer Council NSW 15

20 Head and neck Cancer survival, incidence and mortality in NSW Head and neck cancer This category includes cancers of the tongue, mouth (excluding lip), salivary glands, pharynx, larynx and nasal passages, middle ear and accessory sinuses The five-year relative survival from head and neck cancer in NSW between 1994 and 2000 was 55.2% There was no area variation in the adjusted relative risk of excess death after shrinking due to head and neck cancer There were 4,270 new cases of head and neck cancer in NSW in 1994 to 1998 (3,127 males and 1,143 females) and 1,768 deaths (1,334 males and 434 females) There were more new cases of head and neck cancer than expected after shrinking in Central Sydney (38 in males) and South Eastern Sydney (48 in males), and fewer than expected in Northern Sydney (104 in males) There were 21 more deaths from head and neck cancer than expected after shrinking in males in Central Sydney and 40 fewer than expected in males in Northern Sydney There was no area variation in the excess number of deaths in females Table 6a: Five-year relative survival, relative risk of excess death due to cancer and excess cancer deaths after shrinking with 95% confidence intervals and potential lives savable by Area Health Services in NSW Head and neck cancer Five-year relative Relative risk of excess Excess number of Potential survival (%) death (95% CI) cancer deaths (95% CI) lives savable Central Sydney 55.8 Northern Sydney 57.4 Western Sydney 59.0 Wentworth 57.3 South Western Sydney 56.7 Central Coast 52.6 Hunter 52.3 Illawarra 55.0 South Eastern Sydney 57.9 Northern Rivers 56.1 Mid North Coast 56.2 New England 46.6 Macquarie 46.2 Mid Western 60.7 Far West 32.6 Greater Murray 51.1 Southern 55.9 New South Wales 55.2 There was no area variation in the adjusted relative risks of excess death due to head and neck cancer after shrinking Note: Relative risk of excess death compares observed relative survival with that expected from a Poisson model including terms for age, sex, follow-up year, and spread of disease at diagnosis and age by follow-up year interaction term, with the NSW average rates as the reference. 16 The Cancer Council NSW

21 Cancer survival, incidence and mortality in NSW Head and neck Table 6b: Observed and expected number of new cases and deaths, excess new cases and deaths with 95% confidence intervals, percent of cases aged 65 years or over and with non-localised spread of disease at diagnosis by Area Health Services in NSW Head and neck cancer Males Number of new cases % diagnosed % diagnosed Number of deaths observed expected excess* (95% CI) 65+ yrs as non-localised observed expected excess* (95% CI) Central Sydney (9,67) (3,39) Northern Sydney (-134,-73) (-60,-20) Western Sydney (-29,30) (-25,9) Wentworth (-29,3) (-16,2) South Western Sydney (-40,21) (-6,34) Central Coast (-6,37) (-14,12) Hunter (-24,35) (-6,33) Illawarra (-26,20) (-20,7) South Eastern Sydney (10,86) (-14,32) Northern Rivers (-20,20) (-18,6) Mid North Coast (-25,17) (-15,11) New England (-17,11) (-9,8) Macquarie (-3,17) (-3,9) Mid Western (-12,16) (-8,8) Far West (-3,9) (-1,5) Greater Murray (-16,21) (-5,18) Southern (-16,15) (-11,7) New South Wales Females Number of new cases % diagnosed % diagnosed Number of deaths observed expected excess* (95% CI) 65+ yrs as non-localised observed expected excess* (95% CI) Central Sydney (-1,28) Northern Sydney (-13,28) Western Sydney (-16,14) Wentworth (-10,4) South Western Sydney (-21,9) Central Coast (-2,20) Hunter (-20,9) Illawarra (-16,5) South Eastern Sydney (-7,34) Northern Rivers (-11,7) Mid North Coast (-13,5) New England (-9,3) Macquarie (-4,4) Mid Western (-8,4) Far West (-2,2) Greater Murray (-11,5) Southern (-6,7) New South Wales * Excess number after shrinking. There was no area variation in excess number of deaths The Cancer Council NSW 17

22 Oesophagus Cancer survival, incidence and mortality in NSW Cancer of the oesophagus The five-year relative survival from oesophageal cancer in NSW between 1994 and 2000 was 16.3% No was different from the State average in the adjusted relative risk of excess death after shrinking An additional 92 patients would be expected to survive to five years after diagnosis in NSW if the State average risk was shifted to the 20 th centile of the distribution There were 1,554 new cases of oesophageal cancer in NSW in 1994 to 1998 (961 males and 593 females) and 1,214 deaths (762 males and 452 females) There were more new cases of oesophageal cancer than expected in Mid Western (seven in males) and Southern (nine in males) There were no s in which the number of deaths from oesophageal cancer was different from expected in males, although significant area variation in the excess number of deaths was detected (p = 0.01) There was no area variation in the excess number of deaths after shrinking in females Table 7a: Five-year relative survival, relative risk of excess death due to cancer and excess cancer deaths after shrinking with 95% confidence intervals and potential lives savable by Area Health Services in NSW Cancer of the oesophagus Five-year relative Relative risk of excess Excess number of Potential survival (%) death (95% CI) cancer deaths (95% CI) lives savable Central Sydney ( ) 1 (-9,11) 7 Northern Sydney ( ) 0 (-15,15) 10 Western Sydney ( ) 3 (-6,12) 7 Wentworth ( ) 0 (-5,6) 3 South Western Sydney ( ) -4 (-16,7) 7 Central Coast ( ) 3 (-4,11) 6 Hunter ( ) -3 (-15,10) 8 Illawarra ( ) 1 (-8,10) 6 South Eastern Sydney ( ) 9 (-6,24) 12 Northern Rivers ( ) -5 (-15,5) 5 Mid North Coast ( ) -4 (-12,5) 5 New England ( ) -1 (-7,5) 3 Macquarie ( ) -1 (-4,2) 1 Mid Western ( ) 1 (-3,6) 3 Far West N/A 1.04 ( ) 0 (-1,1) 1 Greater Murray ( ) 0 (-7,7) 5 Southern ( ) -2 (-9,5) 3 New South Wales Note: Relative risk of excess death compares observed relative survival with that expected from a Poisson model including terms for age, sex, follow-up year, and spread of disease at diagnosis and age by follow-up year interaction term, with the NSW average rates as the reference. N/A: No cases alive after the 4 th year of follow-up. 18 The Cancer Council NSW